{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IVWlViNyUoKi"
   },
   "source": [
    "This notebook is accompanied with a [series of blog posts](https://medium.com/@margaretmz/selfie2anime-with-tflite-part-1-overview-f97500800ffe). To follow along with this Colab Notebook we recommend that you also read [this blog post](https://medium.com/@margaretmz/selfie2anime-with-tflite-part-2-tflite-model-84002cf521dc) simultaneously.\n",
    "\n",
    "**Authors**: [Margaret Maynard-Reid](https://twitter.com/margaretmz) and [Sayak Paul](https://twitter.com/RisingSayak) \n",
    "\n",
    "**Reviewers**: [Khanh LeViet](https://twitter.com/khanhlvg) and [Hoi Lam](https://twitter.com/hoitab)\n",
    "\n",
    "Shoutout to Khanh LeViet and Lu Wang from the TensorFlow Lite team for their guidance. Main codebase of UGATIT is here: https://github.com/taki0112/UGATIT. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-YOoYi9oJ1ia"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/margaretmz/selfie2anime-e2e-tutorial/blob/master/ml/Selfie2Anime_Model_Conversion.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/margaretmz/selfie2anime-e2e-tutorial/tree/master/ml/Selfie2Anime_Model_Conversion.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "68KCwL2AWHA0"
   },
   "source": [
    "## Initial setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "24UU4WC8UgVK"
   },
   "outputs": [],
   "source": [
    "!pip install tensorflow==1.14\n",
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VtVH3extW7SW"
   },
   "source": [
    "You can safely ignore the warnings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PIaO7mMJWJGK"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tempfile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3NWRdWuHXB0d"
   },
   "source": [
    "## Loading the checkpoints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1JQcwgXgKkBH"
   },
   "source": [
    "To use the Kaggle API, sign up for a Kaggle account at https://www.kaggle.com. Then go to the 'Account' tab of your user profile (https://www.kaggle.com/account) and select 'Create API Token'. This will trigger the download of `kaggle.json`, a file containing your API credentials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lbb79BcVXBbo"
   },
   "outputs": [],
   "source": [
    "os.environ['KAGGLE_USERNAME'] = \"\" # TODO: enter your Kaggle user name here\n",
    "os.environ['KAGGLE_KEY'] = \"\" # TODO: enter your Kaggle key here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "GxzhumMRXL2x",
    "outputId": "ae40676b-3408-46a8-9e28-d84294f01d2a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading ugatit-selfie2anime-pretrained.zip to /content\n",
      " 99% 1.39G/1.40G [00:18<00:00, 80.7MB/s]\n",
      "100% 1.40G/1.40G [00:18<00:00, 82.2MB/s]\n"
     ]
    }
   ],
   "source": [
    "!kaggle datasets download -d t04glovern/ugatit-selfie2anime-pretrained\n",
    "!unzip -qq /content/ugatit-selfie2anime-pretrained.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: There are other versions of the UGATIT model that you can check [here](https://github.com/taki0112/UGATIT/#pretrained-model). Here, we are using an optimized one. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EW9MMxaaXSbt"
   },
   "source": [
    "## Some utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "YkH8eDfcXN55",
    "outputId": "29d70ee0-155e-46b7-8bd5-a32ced2c63e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'UGATIT'...\n",
      "remote: Enumerating objects: 171, done.\u001b[K\n",
      "remote: Total 171 (delta 0), reused 0 (delta 0), pack-reused 171\u001b[K\n",
      "Receiving objects: 100% (171/171), 5.68 MiB | 35.22 MiB/s, done.\n",
      "Resolving deltas: 100% (89/89), done.\n",
      "/content/UGATIT\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/taki0112/UGATIT\n",
    "%cd UGATIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vFGKYLd-XX8d"
   },
   "outputs": [],
   "source": [
    "# Reference: https://dev.to/0xbf/use-dot-syntax-to-access-dictionary-key-python-tips-10ec\n",
    "class DictX(dict):\n",
    "    def __getattr__(self, key):\n",
    "        try:\n",
    "            return self[key]\n",
    "        except KeyError as k:\n",
    "            raise AttributeError(k)\n",
    "\n",
    "    def __setattr__(self, key, value):\n",
    "        self[key] = value\n",
    "\n",
    "    def __delattr__(self, key):\n",
    "        try:\n",
    "            del self[key]\n",
    "        except KeyError as k:\n",
    "            raise AttributeError(k)\n",
    "\n",
    "    def __repr__(self):\n",
    "        return '<DictX ' + dict.__repr__(self) + '>'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1x062bvJXag7"
   },
   "outputs": [],
   "source": [
    "# This is needed just to initialize `UGATIT` class\n",
    "args = \tdict(phase='test',\n",
    "\tlight=True,\n",
    "\tdataset='selfie2anime',\n",
    "\tepoch=100,\n",
    "\titeration=10000,\n",
    "\tbatch_size=1,\n",
    "\tprint_freq=1000,\n",
    "\tsave_freq=1000,\n",
    "\tdecay_flag=True,\n",
    "\tdecay_epoch=50,\n",
    "\tlr=0.0001,\n",
    "\tGP_ld=10,\n",
    "\tadv_weight=1,\n",
    "\tcycle_weight=10,\n",
    "\tidentity_weight=10,\n",
    "\tcam_weight=1000,\n",
    "\tgan_type='lsgan',\n",
    "\tsmoothing=True,\n",
    "\tch=64,\n",
    "\tn_res=4,\n",
    "\tn_dis=6,\n",
    "\tn_critic=1,\n",
    "\tsn=True,\n",
    "\timg_size=256,\n",
    "\timg_ch=3,\n",
    "\taugment_flag=False,\n",
    "\tcheckpoint_dir='/content',\n",
    "\tresult_dir='/content',\n",
    "\tlog_dir='/content',\n",
    "\tsample_dir='/content')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M_wRn12RXhLO"
   },
   "outputs": [],
   "source": [
    "# Wrap the arguments in a dictionary because this particular format is required \n",
    "# in order to instantiate the `UGATIT` class\n",
    "data = DictX(args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vJAOonpvXwF6"
   },
   "source": [
    "## UGATIT class for convenience\n",
    "\n",
    "Run this block of code to get access to some helper functions. Otherwise, the rest of this Colab may not run correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "Zf1uU4GfXt9a"
   },
   "outputs": [],
   "source": [
    "#@title\n",
    "from ops import *\n",
    "from utils import *\n",
    "from glob import glob\n",
    "import time\n",
    "from tensorflow.contrib.data import prefetch_to_device, shuffle_and_repeat, map_and_batch\n",
    "import numpy as np\n",
    "\n",
    "class UGATIT(object) :\n",
    "    def __init__(self, sess, args):\n",
    "        self.light = args.light\n",
    "\n",
    "        if self.light :\n",
    "            self.model_name = 'UGATIT_light'\n",
    "        else :\n",
    "            self.model_name = 'UGATIT'\n",
    "\n",
    "        self.sess = sess\n",
    "        self.phase = args.phase\n",
    "        self.checkpoint_dir = args.checkpoint_dir\n",
    "        self.result_dir = args.result_dir\n",
    "        self.log_dir = args.log_dir\n",
    "        self.dataset_name = args.dataset\n",
    "        self.augment_flag = args.augment_flag\n",
    "\n",
    "        self.epoch = args.epoch\n",
    "        self.iteration = args.iteration\n",
    "        self.decay_flag = args.decay_flag\n",
    "        self.decay_epoch = args.decay_epoch\n",
    "\n",
    "        self.gan_type = args.gan_type\n",
    "\n",
    "        self.batch_size = args.batch_size\n",
    "        self.print_freq = args.print_freq\n",
    "        self.save_freq = args.save_freq\n",
    "\n",
    "        self.init_lr = args.lr\n",
    "        self.ch = args.ch\n",
    "\n",
    "        \"\"\" Weight \"\"\"\n",
    "        self.adv_weight = args.adv_weight\n",
    "        self.cycle_weight = args.cycle_weight\n",
    "        self.identity_weight = args.identity_weight\n",
    "        self.cam_weight = args.cam_weight\n",
    "        self.ld = args.GP_ld\n",
    "        self.smoothing = args.smoothing\n",
    "\n",
    "        \"\"\" Generator \"\"\"\n",
    "        self.n_res = args.n_res\n",
    "\n",
    "        \"\"\" Discriminator \"\"\"\n",
    "        self.n_dis = args.n_dis\n",
    "        self.n_critic = args.n_critic\n",
    "        self.sn = args.sn\n",
    "\n",
    "        self.img_size = args.img_size\n",
    "        self.img_ch = args.img_ch\n",
    "\n",
    "\n",
    "        self.sample_dir = os.path.join(args.sample_dir, self.model_dir)\n",
    "        check_folder(self.sample_dir)\n",
    "\n",
    "        # self.trainA, self.trainB = prepare_data(dataset_name=self.dataset_name, size=self.img_size\n",
    "        self.trainA_dataset = glob('./dataset/{}/*.*'.format(self.dataset_name + '/trainA'))\n",
    "        self.trainB_dataset = glob('./dataset/{}/*.*'.format(self.dataset_name + '/trainB'))\n",
    "        self.dataset_num = max(len(self.trainA_dataset), len(self.trainB_dataset))\n",
    "\n",
    "        print()\n",
    "\n",
    "        print(\"##### Information #####\")\n",
    "        print(\"# light : \", self.light)\n",
    "        print(\"# gan type : \", self.gan_type)\n",
    "        print(\"# dataset : \", self.dataset_name)\n",
    "        print(\"# max dataset number : \", self.dataset_num)\n",
    "        print(\"# batch_size : \", self.batch_size)\n",
    "        print(\"# epoch : \", self.epoch)\n",
    "        print(\"# iteration per epoch : \", self.iteration)\n",
    "        print(\"# smoothing : \", self.smoothing)\n",
    "\n",
    "        print()\n",
    "\n",
    "        print(\"##### Generator #####\")\n",
    "        print(\"# residual blocks : \", self.n_res)\n",
    "\n",
    "        print()\n",
    "\n",
    "        print(\"##### Discriminator #####\")\n",
    "        print(\"# discriminator layer : \", self.n_dis)\n",
    "        print(\"# the number of critic : \", self.n_critic)\n",
    "        print(\"# spectral normalization : \", self.sn)\n",
    "\n",
    "        print()\n",
    "\n",
    "        print(\"##### Weight #####\")\n",
    "        print(\"# adv_weight : \", self.adv_weight)\n",
    "        print(\"# cycle_weight : \", self.cycle_weight)\n",
    "        print(\"# identity_weight : \", self.identity_weight)\n",
    "        print(\"# cam_weight : \", self.cam_weight)\n",
    "\n",
    "    ##################################################################################\n",
    "    # Generator\n",
    "    ##################################################################################\n",
    "\n",
    "    def generator(self, x_init, reuse=False, scope=\"generator\"):\n",
    "        channel = self.ch\n",
    "        with tf.variable_scope(scope, reuse=reuse) :\n",
    "            x = conv(x_init, channel, kernel=7, stride=1, pad=3, pad_type='reflect', scope='conv')\n",
    "            x = instance_norm(x, scope='ins_norm')\n",
    "            x = relu(x)\n",
    "\n",
    "            # Down-Sampling\n",
    "            for i in range(2) :\n",
    "                x = conv(x, channel*2, kernel=3, stride=2, pad=1, pad_type='reflect', scope='conv_'+str(i))\n",
    "                x = instance_norm(x, scope='ins_norm_'+str(i))\n",
    "                x = relu(x)\n",
    "\n",
    "                channel = channel * 2\n",
    "\n",
    "            # Down-Sampling Bottleneck\n",
    "            for i in range(self.n_res):\n",
    "                x = resblock(x, channel, scope='resblock_' + str(i))\n",
    "\n",
    "\n",
    "            # Class Activation Map\n",
    "            cam_x = global_avg_pooling(x)\n",
    "            cam_gap_logit, cam_x_weight = fully_connected_with_w(cam_x, scope='CAM_logit')\n",
    "            x_gap = tf.multiply(x, cam_x_weight)\n",
    "\n",
    "            cam_x = global_max_pooling(x)\n",
    "            cam_gmp_logit, cam_x_weight = fully_connected_with_w(cam_x, reuse=True, scope='CAM_logit')\n",
    "            x_gmp = tf.multiply(x, cam_x_weight)\n",
    "\n",
    "\n",
    "            cam_logit = tf.concat([cam_gap_logit, cam_gmp_logit], axis=-1)\n",
    "            x = tf.concat([x_gap, x_gmp], axis=-1)\n",
    "\n",
    "            x = conv(x, channel, kernel=1, stride=1, scope='conv_1x1')\n",
    "            x = relu(x)\n",
    "\n",
    "            heatmap = tf.squeeze(tf.reduce_sum(x, axis=-1))\n",
    "\n",
    "            # Gamma, Beta block\n",
    "            gamma, beta = self.MLP(x, reuse=reuse)\n",
    "\n",
    "            # Up-Sampling Bottleneck\n",
    "            for i in range(self.n_res):\n",
    "                x = adaptive_ins_layer_resblock(x, channel, gamma, beta, smoothing=self.smoothing, scope='adaptive_resblock' + str(i))\n",
    "\n",
    "            # Up-Sampling\n",
    "            for i in range(2) :\n",
    "                x = up_sample(x, scale_factor=2)\n",
    "                x = conv(x, channel//2, kernel=3, stride=1, pad=1, pad_type='reflect', scope='up_conv_'+str(i))\n",
    "                x = layer_instance_norm(x, scope='layer_ins_norm_'+str(i))\n",
    "                x = relu(x)\n",
    "\n",
    "                channel = channel // 2\n",
    "\n",
    "\n",
    "            x = conv(x, channels=3, kernel=7, stride=1, pad=3, pad_type='reflect', scope='G_logit')\n",
    "            x = tanh(x)\n",
    "\n",
    "            return x, cam_logit, heatmap\n",
    "\n",
    "    def MLP(self, x, use_bias=True, reuse=False, scope='MLP'):\n",
    "        channel = self.ch * self.n_res\n",
    "\n",
    "        if self.light :\n",
    "            x = global_avg_pooling(x)\n",
    "\n",
    "        with tf.variable_scope(scope, reuse=reuse):\n",
    "            for i in range(2) :\n",
    "                x = fully_connected(x, channel, use_bias, scope='linear_' + str(i))\n",
    "                x = relu(x)\n",
    "\n",
    "\n",
    "            gamma = fully_connected(x, channel, use_bias, scope='gamma')\n",
    "            beta = fully_connected(x, channel, use_bias, scope='beta')\n",
    "\n",
    "            gamma = tf.reshape(gamma, shape=[self.batch_size, 1, 1, channel])\n",
    "            beta = tf.reshape(beta, shape=[self.batch_size, 1, 1, channel])\n",
    "\n",
    "            return gamma, beta\n",
    "\n",
    "    ##################################################################################\n",
    "    # Discriminator\n",
    "    ##################################################################################\n",
    "\n",
    "    def discriminator(self, x_init, reuse=False, scope=\"discriminator\"):\n",
    "        D_logit = []\n",
    "        D_CAM_logit = []\n",
    "        with tf.variable_scope(scope, reuse=reuse) :\n",
    "            local_x, local_cam, local_heatmap = self.discriminator_local(x_init, reuse=reuse, scope='local')\n",
    "            global_x, global_cam, global_heatmap = self.discriminator_global(x_init, reuse=reuse, scope='global')\n",
    "\n",
    "            D_logit.extend([local_x, global_x])\n",
    "            D_CAM_logit.extend([local_cam, global_cam])\n",
    "\n",
    "            return D_logit, D_CAM_logit, local_heatmap, global_heatmap\n",
    "\n",
    "    def discriminator_global(self, x_init, reuse=False, scope='discriminator_global'):\n",
    "        with tf.variable_scope(scope, reuse=reuse):\n",
    "            channel = self.ch\n",
    "            x = conv(x_init, channel, kernel=4, stride=2, pad=1, pad_type='reflect', sn=self.sn, scope='conv_0')\n",
    "            x = lrelu(x, 0.2)\n",
    "\n",
    "            for i in range(1, self.n_dis - 1):\n",
    "                x = conv(x, channel * 2, kernel=4, stride=2, pad=1, pad_type='reflect', sn=self.sn, scope='conv_' + str(i))\n",
    "                x = lrelu(x, 0.2)\n",
    "\n",
    "                channel = channel * 2\n",
    "\n",
    "            x = conv(x, channel * 2, kernel=4, stride=1, pad=1, pad_type='reflect', sn=self.sn, scope='conv_last')\n",
    "            x = lrelu(x, 0.2)\n",
    "\n",
    "            channel = channel * 2\n",
    "\n",
    "            cam_x = global_avg_pooling(x)\n",
    "            cam_gap_logit, cam_x_weight = fully_connected_with_w(cam_x, sn=self.sn, scope='CAM_logit')\n",
    "            x_gap = tf.multiply(x, cam_x_weight)\n",
    "\n",
    "            cam_x = global_max_pooling(x)\n",
    "            cam_gmp_logit, cam_x_weight = fully_connected_with_w(cam_x, sn=self.sn, reuse=True, scope='CAM_logit')\n",
    "            x_gmp = tf.multiply(x, cam_x_weight)\n",
    "\n",
    "            cam_logit = tf.concat([cam_gap_logit, cam_gmp_logit], axis=-1)\n",
    "            x = tf.concat([x_gap, x_gmp], axis=-1)\n",
    "\n",
    "            x = conv(x, channel, kernel=1, stride=1, scope='conv_1x1')\n",
    "            x = lrelu(x, 0.2)\n",
    "\n",
    "            heatmap = tf.squeeze(tf.reduce_sum(x, axis=-1))\n",
    "\n",
    "\n",
    "            x = conv(x, channels=1, kernel=4, stride=1, pad=1, pad_type='reflect', sn=self.sn, scope='D_logit')\n",
    "\n",
    "            return x, cam_logit, heatmap\n",
    "\n",
    "    def discriminator_local(self, x_init, reuse=False, scope='discriminator_local'):\n",
    "        with tf.variable_scope(scope, reuse=reuse) :\n",
    "            channel = self.ch\n",
    "            x = conv(x_init, channel, kernel=4, stride=2, pad=1, pad_type='reflect', sn=self.sn, scope='conv_0')\n",
    "            x = lrelu(x, 0.2)\n",
    "\n",
    "            for i in range(1, self.n_dis - 2 - 1):\n",
    "                x = conv(x, channel * 2, kernel=4, stride=2, pad=1, pad_type='reflect', sn=self.sn, scope='conv_' + str(i))\n",
    "                x = lrelu(x, 0.2)\n",
    "\n",
    "                channel = channel * 2\n",
    "\n",
    "            x = conv(x, channel * 2, kernel=4, stride=1, pad=1, pad_type='reflect', sn=self.sn, scope='conv_last')\n",
    "            x = lrelu(x, 0.2)\n",
    "\n",
    "            channel = channel * 2\n",
    "\n",
    "            cam_x = global_avg_pooling(x)\n",
    "            cam_gap_logit, cam_x_weight = fully_connected_with_w(cam_x, sn=self.sn, scope='CAM_logit')\n",
    "            x_gap = tf.multiply(x, cam_x_weight)\n",
    "\n",
    "            cam_x = global_max_pooling(x)\n",
    "            cam_gmp_logit, cam_x_weight = fully_connected_with_w(cam_x, sn=self.sn, reuse=True, scope='CAM_logit')\n",
    "            x_gmp = tf.multiply(x, cam_x_weight)\n",
    "\n",
    "            cam_logit = tf.concat([cam_gap_logit, cam_gmp_logit], axis=-1)\n",
    "            x = tf.concat([x_gap, x_gmp], axis=-1)\n",
    "\n",
    "            x = conv(x, channel, kernel=1, stride=1, scope='conv_1x1')\n",
    "            x = lrelu(x, 0.2)\n",
    "\n",
    "            heatmap = tf.squeeze(tf.reduce_sum(x, axis=-1))\n",
    "\n",
    "            x = conv(x, channels=1, kernel=4, stride=1, pad=1, pad_type='reflect', sn=self.sn, scope='D_logit')\n",
    "\n",
    "            return x, cam_logit, heatmap\n",
    "\n",
    "    ##################################################################################\n",
    "    # Model\n",
    "    ##################################################################################\n",
    "\n",
    "    def generate_a2b(self, x_A, reuse=False):\n",
    "        out, cam, _ = self.generator(x_A, reuse=reuse, scope=\"generator_B\")\n",
    "\n",
    "        return out, cam\n",
    "\n",
    "    def generate_b2a(self, x_B, reuse=False):\n",
    "        out, cam, _ = self.generator(x_B, reuse=reuse, scope=\"generator_A\")\n",
    "\n",
    "        return out, cam\n",
    "\n",
    "    def discriminate_real(self, x_A, x_B):\n",
    "        real_A_logit, real_A_cam_logit, _, _ = self.discriminator(x_A, scope=\"discriminator_A\")\n",
    "        real_B_logit, real_B_cam_logit, _, _ = self.discriminator(x_B, scope=\"discriminator_B\")\n",
    "\n",
    "        return real_A_logit, real_A_cam_logit, real_B_logit, real_B_cam_logit\n",
    "\n",
    "    def discriminate_fake(self, x_ba, x_ab):\n",
    "        fake_A_logit, fake_A_cam_logit, _, _ = self.discriminator(x_ba, reuse=True, scope=\"discriminator_A\")\n",
    "        fake_B_logit, fake_B_cam_logit, _, _ = self.discriminator(x_ab, reuse=True, scope=\"discriminator_B\")\n",
    "\n",
    "        return fake_A_logit, fake_A_cam_logit, fake_B_logit, fake_B_cam_logit\n",
    "\n",
    "    def gradient_panalty(self, real, fake, scope=\"discriminator_A\"):\n",
    "        if self.gan_type.__contains__('dragan'):\n",
    "            eps = tf.random_uniform(shape=tf.shape(real), minval=0., maxval=1.)\n",
    "            _, x_var = tf.nn.moments(real, axes=[0, 1, 2, 3])\n",
    "            x_std = tf.sqrt(x_var)  # magnitude of noise decides the size of local region\n",
    "\n",
    "            fake = real + 0.5 * x_std * eps\n",
    "\n",
    "        alpha = tf.random_uniform(shape=[self.batch_size, 1, 1, 1], minval=0., maxval=1.)\n",
    "        interpolated = real + alpha * (fake - real)\n",
    "\n",
    "        logit, cam_logit, _, _ = self.discriminator(interpolated, reuse=True, scope=scope)\n",
    "\n",
    "\n",
    "        GP = []\n",
    "        cam_GP = []\n",
    "\n",
    "        for i in range(2) :\n",
    "            grad = tf.gradients(logit[i], interpolated)[0] # gradient of D(interpolated)\n",
    "            grad_norm = tf.norm(flatten(grad), axis=1) # l2 norm\n",
    "\n",
    "            # WGAN - LP\n",
    "            if self.gan_type == 'wgan-lp' :\n",
    "                GP.append(self.ld * tf.reduce_mean(tf.square(tf.maximum(0.0, grad_norm - 1.))))\n",
    "\n",
    "            elif self.gan_type == 'wgan-gp' or self.gan_type == 'dragan':\n",
    "                GP.append(self.ld * tf.reduce_mean(tf.square(grad_norm - 1.)))\n",
    "\n",
    "        for i in range(2) :\n",
    "            grad = tf.gradients(cam_logit[i], interpolated)[0] # gradient of D(interpolated)\n",
    "            grad_norm = tf.norm(flatten(grad), axis=1) # l2 norm\n",
    "\n",
    "            # WGAN - LP\n",
    "            if self.gan_type == 'wgan-lp' :\n",
    "                cam_GP.append(self.ld * tf.reduce_mean(tf.square(tf.maximum(0.0, grad_norm - 1.))))\n",
    "\n",
    "            elif self.gan_type == 'wgan-gp' or self.gan_type == 'dragan':\n",
    "                cam_GP.append(self.ld * tf.reduce_mean(tf.square(grad_norm - 1.)))\n",
    "\n",
    "\n",
    "        return sum(GP), sum(cam_GP)\n",
    "\n",
    "    def build_model(self):\n",
    "        if self.phase == 'train' :\n",
    "            self.lr = tf.placeholder(tf.float32, name='learning_rate')\n",
    "\n",
    "\n",
    "            \"\"\" Input Image\"\"\"\n",
    "            Image_Data_Class = ImageData(self.img_size, self.img_ch, self.augment_flag)\n",
    "\n",
    "            trainA = tf.data.Dataset.from_tensor_slices(self.trainA_dataset)\n",
    "            trainB = tf.data.Dataset.from_tensor_slices(self.trainB_dataset)\n",
    "\n",
    "\n",
    "            gpu_device = '/gpu:0'\n",
    "            trainA = trainA.apply(shuffle_and_repeat(self.dataset_num)).apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=16, drop_remainder=True)).apply(prefetch_to_device(gpu_device, None))\n",
    "            trainB = trainB.apply(shuffle_and_repeat(self.dataset_num)).apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=16, drop_remainder=True)).apply(prefetch_to_device(gpu_device, None))\n",
    "\n",
    "\n",
    "            trainA_iterator = trainA.make_one_shot_iterator()\n",
    "            trainB_iterator = trainB.make_one_shot_iterator()\n",
    "\n",
    "            self.domain_A = trainA_iterator.get_next()\n",
    "            self.domain_B = trainB_iterator.get_next()\n",
    "\n",
    "            \"\"\" Define Generator, Discriminator \"\"\"\n",
    "            x_ab, cam_ab = self.generate_a2b(self.domain_A) # real a\n",
    "            x_ba, cam_ba = self.generate_b2a(self.domain_B) # real b\n",
    "\n",
    "            x_aba, _ = self.generate_b2a(x_ab, reuse=True) # real b\n",
    "            x_bab, _ = self.generate_a2b(x_ba, reuse=True) # real a\n",
    "\n",
    "            x_aa, cam_aa = self.generate_b2a(self.domain_A, reuse=True) # fake b\n",
    "            x_bb, cam_bb = self.generate_a2b(self.domain_B, reuse=True) # fake a\n",
    "\n",
    "            real_A_logit, real_A_cam_logit, real_B_logit, real_B_cam_logit = self.discriminate_real(self.domain_A, self.domain_B)\n",
    "            fake_A_logit, fake_A_cam_logit, fake_B_logit, fake_B_cam_logit = self.discriminate_fake(x_ba, x_ab)\n",
    "\n",
    "\n",
    "            \"\"\" Define Loss \"\"\"\n",
    "            if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan' :\n",
    "                GP_A, GP_CAM_A = self.gradient_panalty(real=self.domain_A, fake=x_ba, scope=\"discriminator_A\")\n",
    "                GP_B, GP_CAM_B = self.gradient_panalty(real=self.domain_B, fake=x_ab, scope=\"discriminator_B\")\n",
    "            else :\n",
    "                GP_A, GP_CAM_A  = 0, 0\n",
    "                GP_B, GP_CAM_B = 0, 0\n",
    "\n",
    "            G_ad_loss_A = (generator_loss(self.gan_type, fake_A_logit) + generator_loss(self.gan_type, fake_A_cam_logit))\n",
    "            G_ad_loss_B = (generator_loss(self.gan_type, fake_B_logit) + generator_loss(self.gan_type, fake_B_cam_logit))\n",
    "\n",
    "            D_ad_loss_A = (discriminator_loss(self.gan_type, real_A_logit, fake_A_logit) + discriminator_loss(self.gan_type, real_A_cam_logit, fake_A_cam_logit) + GP_A + GP_CAM_A)\n",
    "            D_ad_loss_B = (discriminator_loss(self.gan_type, real_B_logit, fake_B_logit) + discriminator_loss(self.gan_type, real_B_cam_logit, fake_B_cam_logit) + GP_B + GP_CAM_B)\n",
    "\n",
    "            reconstruction_A = L1_loss(x_aba, self.domain_A) # reconstruction\n",
    "            reconstruction_B = L1_loss(x_bab, self.domain_B) # reconstruction\n",
    "\n",
    "            identity_A = L1_loss(x_aa, self.domain_A)\n",
    "            identity_B = L1_loss(x_bb, self.domain_B)\n",
    "\n",
    "            cam_A = cam_loss(source=cam_ba, non_source=cam_aa)\n",
    "            cam_B = cam_loss(source=cam_ab, non_source=cam_bb)\n",
    "\n",
    "            Generator_A_gan = self.adv_weight * G_ad_loss_A\n",
    "            Generator_A_cycle = self.cycle_weight * reconstruction_B\n",
    "            Generator_A_identity = self.identity_weight * identity_A\n",
    "            Generator_A_cam = self.cam_weight * cam_A\n",
    "\n",
    "\n",
    "            Generator_B_gan = self.adv_weight * G_ad_loss_B\n",
    "            Generator_B_cycle = self.cycle_weight * reconstruction_A\n",
    "            Generator_B_identity = self.identity_weight * identity_B\n",
    "            Generator_B_cam = self.cam_weight * cam_B\n",
    "\n",
    "\n",
    "            Generator_A_loss = Generator_A_gan + Generator_A_cycle + Generator_A_identity + Generator_A_cam\n",
    "            Generator_B_loss = Generator_B_gan + Generator_B_cycle + Generator_B_identity + Generator_B_cam\n",
    "\n",
    "\n",
    "            Discriminator_A_loss = self.adv_weight * D_ad_loss_A\n",
    "            Discriminator_B_loss = self.adv_weight * D_ad_loss_B\n",
    "\n",
    "            self.Generator_loss = Generator_A_loss + Generator_B_loss + regularization_loss('generator')\n",
    "            self.Discriminator_loss = Discriminator_A_loss + Discriminator_B_loss + regularization_loss('discriminator')\n",
    "\n",
    "\n",
    "            \"\"\" Result Image \"\"\"\n",
    "            self.fake_A = x_ba\n",
    "            self.fake_B = x_ab\n",
    "\n",
    "            self.real_A = self.domain_A\n",
    "            self.real_B = self.domain_B\n",
    "\n",
    "\n",
    "            \"\"\" Training \"\"\"\n",
    "            t_vars = tf.trainable_variables()\n",
    "            G_vars = [var for var in t_vars if 'generator' in var.name]\n",
    "            D_vars = [var for var in t_vars if 'discriminator' in var.name]\n",
    "\n",
    "            self.G_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.Generator_loss, var_list=G_vars)\n",
    "            self.D_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.Discriminator_loss, var_list=D_vars)\n",
    "\n",
    "\n",
    "            \"\"\"\" Summary \"\"\"\n",
    "            self.all_G_loss = tf.summary.scalar(\"Generator_loss\", self.Generator_loss)\n",
    "            self.all_D_loss = tf.summary.scalar(\"Discriminator_loss\", self.Discriminator_loss)\n",
    "\n",
    "            self.G_A_loss = tf.summary.scalar(\"G_A_loss\", Generator_A_loss)\n",
    "            self.G_A_gan = tf.summary.scalar(\"G_A_gan\", Generator_A_gan)\n",
    "            self.G_A_cycle = tf.summary.scalar(\"G_A_cycle\", Generator_A_cycle)\n",
    "            self.G_A_identity = tf.summary.scalar(\"G_A_identity\", Generator_A_identity)\n",
    "            self.G_A_cam = tf.summary.scalar(\"G_A_cam\", Generator_A_cam)\n",
    "\n",
    "            self.G_B_loss = tf.summary.scalar(\"G_B_loss\", Generator_B_loss)\n",
    "            self.G_B_gan = tf.summary.scalar(\"G_B_gan\", Generator_B_gan)\n",
    "            self.G_B_cycle = tf.summary.scalar(\"G_B_cycle\", Generator_B_cycle)\n",
    "            self.G_B_identity = tf.summary.scalar(\"G_B_identity\", Generator_B_identity)\n",
    "            self.G_B_cam = tf.summary.scalar(\"G_B_cam\", Generator_B_cam)\n",
    "\n",
    "            self.D_A_loss = tf.summary.scalar(\"D_A_loss\", Discriminator_A_loss)\n",
    "            self.D_B_loss = tf.summary.scalar(\"D_B_loss\", Discriminator_B_loss)\n",
    "\n",
    "            self.rho_var = []\n",
    "            for var in tf.trainable_variables():\n",
    "                if 'rho' in var.name:\n",
    "                    self.rho_var.append(tf.summary.histogram(var.name, var))\n",
    "                    self.rho_var.append(tf.summary.scalar(var.name + \"_min\", tf.reduce_min(var)))\n",
    "                    self.rho_var.append(tf.summary.scalar(var.name + \"_max\", tf.reduce_max(var)))\n",
    "                    self.rho_var.append(tf.summary.scalar(var.name + \"_mean\", tf.reduce_mean(var)))\n",
    "\n",
    "            g_summary_list = [self.G_A_loss, self.G_A_gan, self.G_A_cycle, self.G_A_identity, self.G_A_cam,\n",
    "                              self.G_B_loss, self.G_B_gan, self.G_B_cycle, self.G_B_identity, self.G_B_cam,\n",
    "                              self.all_G_loss]\n",
    "\n",
    "            g_summary_list.extend(self.rho_var)\n",
    "            d_summary_list = [self.D_A_loss, self.D_B_loss, self.all_D_loss]\n",
    "\n",
    "            self.G_loss = tf.summary.merge(g_summary_list)\n",
    "            self.D_loss = tf.summary.merge(d_summary_list)\n",
    "\n",
    "        else :\n",
    "            \"\"\" Test \"\"\"\n",
    "            self.test_domain_A = tf.placeholder(tf.float32, [1, self.img_size, self.img_size, self.img_ch], name='test_domain_A')\n",
    "            self.test_domain_B = tf.placeholder(tf.float32, [1, self.img_size, self.img_size, self.img_ch], name='test_domain_B')\n",
    "\n",
    "\n",
    "            self.test_fake_B, _ = self.generate_a2b(self.test_domain_A)\n",
    "            self.test_fake_A, _ = self.generate_b2a(self.test_domain_B)\n",
    "\n",
    "\n",
    "    def train(self):\n",
    "        # initialize all variables\n",
    "        tf.global_variables_initializer().run()\n",
    "\n",
    "        # saver to save model\n",
    "        self.saver = tf.train.Saver()\n",
    "\n",
    "        # summary writer\n",
    "        self.writer = tf.summary.FileWriter(self.log_dir + '/' + self.model_dir, self.sess.graph)\n",
    "\n",
    "\n",
    "        # restore check-point if it exits\n",
    "        could_load, checkpoint_counter = self.load(self.checkpoint_dir)\n",
    "        if could_load:\n",
    "            start_epoch = (int)(checkpoint_counter / self.iteration)\n",
    "            start_batch_id = checkpoint_counter - start_epoch * self.iteration\n",
    "            counter = checkpoint_counter\n",
    "            print(\" [*] Load SUCCESS\")\n",
    "        else:\n",
    "            start_epoch = 0\n",
    "            start_batch_id = 0\n",
    "            counter = 1\n",
    "            print(\" [!] Load failed...\")\n",
    "\n",
    "        # loop for epoch\n",
    "        start_time = time.time()\n",
    "        past_g_loss = -1.\n",
    "        lr = self.init_lr\n",
    "        for epoch in range(start_epoch, self.epoch):\n",
    "            # lr = self.init_lr if epoch < self.decay_epoch else self.init_lr * (self.epoch - epoch) / (self.epoch - self.decay_epoch)\n",
    "            if self.decay_flag :\n",
    "                #lr = self.init_lr * pow(0.5, epoch // self.decay_epoch)\n",
    "                lr = self.init_lr if epoch < self.decay_epoch else self.init_lr * (self.epoch - epoch) / (self.epoch - self.decay_epoch)\n",
    "            for idx in range(start_batch_id, self.iteration):\n",
    "                train_feed_dict = {\n",
    "                    self.lr : lr\n",
    "                }\n",
    "\n",
    "                # Update D\n",
    "                _, d_loss, summary_str = self.sess.run([self.D_optim,\n",
    "                                                        self.Discriminator_loss, self.D_loss], feed_dict = train_feed_dict)\n",
    "                self.writer.add_summary(summary_str, counter)\n",
    "\n",
    "                # Update G\n",
    "                g_loss = None\n",
    "                if (counter - 1) % self.n_critic == 0 :\n",
    "                    batch_A_images, batch_B_images, fake_A, fake_B, _, g_loss, summary_str = self.sess.run([self.real_A, self.real_B,\n",
    "                                                                                                            self.fake_A, self.fake_B,\n",
    "                                                                                                            self.G_optim,\n",
    "                                                                                                            self.Generator_loss, self.G_loss], feed_dict = train_feed_dict)\n",
    "                    self.writer.add_summary(summary_str, counter)\n",
    "                    past_g_loss = g_loss\n",
    "\n",
    "                # display training status\n",
    "                counter += 1\n",
    "                if g_loss == None :\n",
    "                    g_loss = past_g_loss\n",
    "                print(\"Epoch: [%2d] [%5d/%5d] time: %4.4f d_loss: %.8f, g_loss: %.8f\" % (epoch, idx, self.iteration, time.time() - start_time, d_loss, g_loss))\n",
    "\n",
    "                if np.mod(idx+1, self.print_freq) == 0 :\n",
    "                    save_images(batch_A_images, [self.batch_size, 1],\n",
    "                                './{}/real_A_{:03d}_{:05d}.png'.format(self.sample_dir, epoch, idx+1))\n",
    "                    # save_images(batch_B_images, [self.batch_size, 1],\n",
    "                    #             './{}/real_B_{:03d}_{:05d}.png'.format(self.sample_dir, epoch, idx+1))\n",
    "\n",
    "                    # save_images(fake_A, [self.batch_size, 1],\n",
    "                    #             './{}/fake_A_{:03d}_{:05d}.png'.format(self.sample_dir, epoch, idx+1))\n",
    "                    save_images(fake_B, [self.batch_size, 1],\n",
    "                                './{}/fake_B_{:03d}_{:05d}.png'.format(self.sample_dir, epoch, idx+1))\n",
    "\n",
    "                if np.mod(idx + 1, self.save_freq) == 0:\n",
    "                    self.save(self.checkpoint_dir, counter)\n",
    "\n",
    "\n",
    "\n",
    "            # After an epoch, start_batch_id is set to zero\n",
    "            # non-zero value is only for the first epoch after loading pre-trained model\n",
    "            start_batch_id = 0\n",
    "\n",
    "            # save model for final step\n",
    "            self.save(self.checkpoint_dir, counter)\n",
    "\n",
    "    @property\n",
    "    def model_dir(self):\n",
    "        n_res = str(self.n_res) + 'resblock'\n",
    "        n_dis = str(self.n_dis) + 'dis'\n",
    "\n",
    "        if self.smoothing :\n",
    "            smoothing = '_smoothing'\n",
    "        else :\n",
    "            smoothing = ''\n",
    "\n",
    "        if self.sn :\n",
    "            sn = '_sn'\n",
    "        else :\n",
    "            sn = ''\n",
    "\n",
    "        return \"{}_{}_{}_{}_{}_{}_{}_{}_{}_{}{}{}\".format(self.model_name, self.dataset_name,\n",
    "                                                         self.gan_type, n_res, n_dis,\n",
    "                                                         self.n_critic,\n",
    "                                                         self.adv_weight, self.cycle_weight, self.identity_weight, self.cam_weight, sn, smoothing)\n",
    "\n",
    "    def save(self, checkpoint_dir, step):\n",
    "        checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir)\n",
    "\n",
    "        if not os.path.exists(checkpoint_dir):\n",
    "            os.makedirs(checkpoint_dir)\n",
    "\n",
    "        self.saver.save(self.sess, os.path.join(checkpoint_dir, self.model_name + '.model'), global_step=step)\n",
    "\n",
    "    def load(self, checkpoint_dir):\n",
    "        print(\" [*] Reading checkpoints...\")\n",
    "        checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir)\n",
    "\n",
    "        ckpt = tf.train.get_checkpoint_state(checkpoint_dir)\n",
    "        if ckpt and ckpt.model_checkpoint_path:\n",
    "            ckpt_name = os.path.basename(ckpt.model_checkpoint_path)\n",
    "            self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))\n",
    "            counter = int(ckpt_name.split('-')[-1])\n",
    "            print(\" [*] Success to read {}\".format(ckpt_name))\n",
    "            return True, counter\n",
    "        else:\n",
    "            print(\" [*] Failed to find a checkpoint\")\n",
    "            return False, 0\n",
    "\n",
    "    def test(self):\n",
    "        tf.global_variables_initializer().run()\n",
    "        test_A_files = glob('./dataset/{}/*.*'.format(self.dataset_name + '/testA'))\n",
    "        test_B_files = glob('./dataset/{}/*.*'.format(self.dataset_name + '/testB'))\n",
    "\n",
    "        self.saver = tf.train.Saver()\n",
    "        could_load, checkpoint_counter = self.load(self.checkpoint_dir)\n",
    "        self.result_dir = os.path.join(self.result_dir, self.model_dir)\n",
    "        check_folder(self.result_dir)\n",
    "\n",
    "        if could_load :\n",
    "            print(\" [*] Load SUCCESS\")\n",
    "        else :\n",
    "            print(\" [!] Load failed...\")\n",
    "\n",
    "        # write html for visual comparison\n",
    "        index_path = os.path.join(self.result_dir, 'index.html')\n",
    "        index = open(index_path, 'w')\n",
    "        index.write(\"<html><body><table><tr>\")\n",
    "        index.write(\"<th>name</th><th>input</th><th>output</th></tr>\")\n",
    "\n",
    "        for sample_file  in test_A_files : # A -> B\n",
    "            print('Processing A image: ' + sample_file)\n",
    "            sample_image = np.asarray(load_test_data(sample_file, size=self.img_size))\n",
    "            image_path = os.path.join(self.result_dir,'{0}'.format(os.path.basename(sample_file)))\n",
    "\n",
    "            fake_img = self.sess.run(self.test_fake_B, feed_dict = {self.test_domain_A : sample_image})\n",
    "            save_images(fake_img, [1, 1], image_path)\n",
    "\n",
    "            index.write(\"<td>%s</td>\" % os.path.basename(image_path))\n",
    "\n",
    "            index.write(\"<td><img src='%s' width='%d' height='%d'></td>\" % (sample_file if os.path.isabs(sample_file) else (\n",
    "                '../..' + os.path.sep + sample_file), self.img_size, self.img_size))\n",
    "            index.write(\"<td><img src='%s' width='%d' height='%d'></td>\" % (image_path if os.path.isabs(image_path) else (\n",
    "                '../..' + os.path.sep + image_path), self.img_size, self.img_size))\n",
    "            index.write(\"</tr>\")\n",
    "\n",
    "        for sample_file  in test_B_files : # B -> A\n",
    "            print('Processing B image: ' + sample_file)\n",
    "            sample_image = np.asarray(load_test_data(sample_file, size=self.img_size))\n",
    "            image_path = os.path.join(self.result_dir,'{0}'.format(os.path.basename(sample_file)))\n",
    "\n",
    "            fake_img = self.sess.run(self.test_fake_A, feed_dict = {self.test_domain_B : sample_image})\n",
    "\n",
    "            save_images(fake_img, [1, 1], image_path)\n",
    "            index.write(\"<td>%s</td>\" % os.path.basename(image_path))\n",
    "            index.write(\"<td><img src='%s' width='%d' height='%d'></td>\" % (sample_file if os.path.isabs(sample_file) else (\n",
    "                    '../..' + os.path.sep + sample_file), self.img_size, self.img_size))\n",
    "            index.write(\"<td><img src='%s' width='%d' height='%d'></td>\" % (image_path if os.path.isabs(image_path) else (\n",
    "                    '../..' + os.path.sep + image_path), self.img_size, self.img_size))\n",
    "            index.write(\"</tr>\")\n",
    "        index.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UB6s-i19X4YD"
   },
   "source": [
    "## Build and initialize the model\n",
    "\n",
    "[Reference](https://github.com/tensorflow/magenta/blob/85ef5267513f62f4a40b01b2a1ee488f90f64a13/magenta/models/arbitrary_image_stylization/arbitrary_image_stylization_convert_tflite.py#L46) of the following utility. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uUP3tXMJX2Yt"
   },
   "outputs": [],
   "source": [
    "def load_checkpoint(sess, checkpoint):\n",
    "  \"\"\"Loads a checkpoint file into the session.\n",
    "  Args:\n",
    "    sess: tf.Session, the TF session to load variables from the checkpoint to.\n",
    "    checkpoint: str, path to the checkpoint file.\n",
    "  \"\"\"\n",
    "  model_saver = tf.train.Saver(tf.global_variables())\n",
    "  checkpoint = os.path.expanduser(checkpoint)\n",
    "  if tf.gfile.IsDirectory(checkpoint):\n",
    "    checkpoint = tf.train.latest_checkpoint(checkpoint)\n",
    "    tf.logging.info('loading latest checkpoint file: {}'.format(checkpoint))\n",
    "  model_saver.restore(sess, checkpoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qSyn5u-oYb2s"
   },
   "source": [
    "## Exporting to `SavedModel`\n",
    "\n",
    "Note that we will only be using the `Selfie2Anime` variant. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "ebgyMVAVYX0D",
    "outputId": "a2c6188b-a4ac-4c6b-9cfe-e4fb4628b713"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##### Information #####\n",
      "# light :  True\n",
      "# gan type :  lsgan\n",
      "# dataset :  selfie2anime\n",
      "# max dataset number :  0\n",
      "# batch_size :  1\n",
      "# epoch :  100\n",
      "# iteration per epoch :  10000\n",
      "# smoothing :  True\n",
      "\n",
      "##### Generator #####\n",
      "# residual blocks :  4\n",
      "\n",
      "##### Discriminator #####\n",
      "# discriminator layer :  6\n",
      "# the number of critic :  1\n",
      "# spectral normalization :  True\n",
      "\n",
      "##### Weight #####\n",
      "# adv_weight :  1\n",
      "# cycle_weight :  10\n",
      "# identity_weight :  10\n",
      "# cam_weight :  1000\n",
      "WARNING:tensorflow:From /content/UGATIT/ops.py:17: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "WARNING:tensorflow:From /content/UGATIT/ops.py:49: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.keras.layers.Conv2D` instead.\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0798e9cc0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0798e9cc0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0798e9cc0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0798e9cc0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0798e9f98>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0798e9f98>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0798e9f98>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0798e9f98>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0798e9cc0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0798e9cc0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0798e9cc0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0798e9cc0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0601f0080>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0601f0080>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0601f0080>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb0601f0080>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb079ab9cf8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb079ab9cf8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb079ab9cf8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb079ab9cf8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb06011a0f0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb06011a0f0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb06011a0f0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb06011a0f0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb079a118d0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb079a118d0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb079a118d0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb079a118d0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb079a118d0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb079a118d0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb079a118d0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb079a118d0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb060005748>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb060005748>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb060005748>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb060005748>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ff6e080>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ff6e080>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ff6e080>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ff6e080>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ff6eef0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ff6eef0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ff6eef0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ff6eef0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:From /content/UGATIT/ops.py:105: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.flatten instead.\n",
      "WARNING:tensorflow:Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb0601ddb70>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb0601ddb70>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb0601ddb70>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb0601ddb70>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING:tensorflow:From /content/UGATIT/ops.py:61: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
      "\n",
      "WARNING:tensorflow:Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05ff6e780>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05ff6e780>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05ff6e780>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05ff6e780>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fdc1550>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fdc1550>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fdc1550>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fdc1550>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fe35a58>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fe35a58>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fe35a58>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fe35a58>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING:tensorflow:From /content/UGATIT/ops.py:100: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dense instead.\n",
      "WARNING:tensorflow:Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fe35c18>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fe35c18>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fe35c18>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fe35c18>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fd97a20>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fd97a20>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fd97a20>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fd97a20>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING:tensorflow:Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fd97ba8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fd97ba8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fd97ba8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fd97ba8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fd97748>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fd97748>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fd97748>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fd97748>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING:tensorflow:Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fd67550>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fd67550>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fd67550>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fd67550>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fe64978>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fe64978>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fe64978>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05fe64978>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING:tensorflow:Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fe35be0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fe35be0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fe35be0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05fe35be0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd97d30>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd97d30>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd97d30>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd97d30>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd37c18>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd37c18>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd37c18>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd37c18>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd37c18>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd37c18>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd37c18>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd37c18>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fc0e8d0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fc0e8d0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fc0e8d0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fc0e8d0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fbf3fd0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fbf3fd0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fbf3fd0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fbf3fd0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fafc630>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fafc630>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fafc630>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fafc630>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fb296a0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fb296a0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fb296a0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fb296a0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f9c37f0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f9c37f0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f9c37f0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f9c37f0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:From /content/UGATIT/ops.py:145: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
      "\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fdf5908>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fdf5908>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fdf5908>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fdf5908>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f923f60>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f923f60>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f923f60>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f923f60>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f8d54a8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f8d54a8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f8d54a8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f8d54a8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f851128>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f851128>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f851128>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f851128>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f8806a0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f8806a0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f8806a0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f8806a0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f880b00>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f880b00>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f880b00>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f880b00>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f95b5c0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f95b5c0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f95b5c0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f95b5c0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd376d8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd376d8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd376d8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd376d8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f6f2cf8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f6f2cf8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f6f2cf8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f6f2cf8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f6f2be0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f6f2be0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f6f2be0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f6f2be0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f6f2be0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f6f2be0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f6f2be0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f6f2be0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f5eaf98>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f5eaf98>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f5eaf98>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f5eaf98>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f5eaf98>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f5eaf98>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f5eaf98>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f5eaf98>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fcfb080>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fcfb080>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fcfb080>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fcfb080>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb0798e9ba8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb0798e9ba8>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb0798e9ba8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb0798e9ba8>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING:tensorflow:Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f58b5f8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f58b5f8>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f58b5f8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f58b5f8>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb06010f3c8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb06010f3c8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb06010f3c8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb06010f3c8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f34f550>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f34f550>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f34f550>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f34f550>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING:tensorflow:Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f34f208>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f34f208>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f34f208>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f34f208>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f363c18>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f363c18>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f363c18>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f363c18>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING:tensorflow:Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f363f28>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f363f28>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f363f28>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f363f28>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f363390>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f363390>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f363390>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f363390>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING:tensorflow:Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f38dd30>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f38dd30>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f38dd30>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f38dd30>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f363668>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f363668>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f363668>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Flatten.call of <tensorflow.python.layers.core.Flatten object at 0x7fb05f363668>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING:tensorflow:Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f363940>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f363940>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f363940>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7fb05f363940>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2e5c0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2e5c0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2e5c0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2e5c0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2e5c0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2e5c0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2e5c0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2e5c0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2e240>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2e240>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2e240>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2e240>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2ef98>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2ef98>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2ef98>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05fd2ef98>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f172940>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f172940>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f172940>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f172940>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f12ecf8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f12ecf8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f12ecf8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f12ecf8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f363c50>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f363c50>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f363c50>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f363c50>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f363f28>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f363f28>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f363f28>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f363f28>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f34fa90>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f34fa90>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f34fa90>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05f34fa90>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ee7bac8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ee7bac8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ee7bac8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ee7bac8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ee7be10>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ee7be10>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ee7be10>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x7fb05ee7be10>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "INFO:tensorflow:loading latest checkpoint file: /content/checkpoint/UGATIT_light_selfie2anime_lsgan_4resblock_6dis_1_1_10_10_1000_sn_smoothing/UGATIT_light.model-214000\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "INFO:tensorflow:Restoring parameters from /content/checkpoint/UGATIT_light_selfie2anime_lsgan_4resblock_6dis_1_1_10_10_1000_sn_smoothing/UGATIT_light.model-214000\n",
      "WARNING:tensorflow:From <ipython-input-11-d95cd498b5a1>:15: simple_save (from tensorflow.python.saved_model.simple_save) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.simple_save.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
      "INFO:tensorflow:Assets added to graph.\n",
      "INFO:tensorflow:No assets to write.\n",
      "INFO:tensorflow:SavedModel written to: /tmp/tmp22_x9l4i/saved_model.pb\n"
     ]
    }
   ],
   "source": [
    "saved_model_dir = tempfile.mkdtemp()\n",
    "\n",
    "with tf.Graph().as_default(), tf.Session() as sess:\n",
    "    gan = UGATIT(sess, data)\n",
    "    gan.build_model()\n",
    "    load_checkpoint(sess, '/content/checkpoint/UGATIT_light_selfie2anime_lsgan_4resblock_6dis_1_1_10_10_1000_sn_smoothing')\n",
    "    \n",
    "    # Write SavedModel for serving or conversion to TF Lite\n",
    "    tf.saved_model.simple_save(\n",
    "        sess,\n",
    "        saved_model_dir,\n",
    "        inputs={\n",
    "            gan.test_domain_A.name: gan.test_domain_A,\n",
    "        },\n",
    "        outputs={gan.test_fake_B.name: gan.test_fake_B})\n",
    "    tf.logging.debug('Export transform SavedModel to',\n",
    "                     saved_model_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4zZLimW7Yrl8"
   },
   "source": [
    "Note the path of the `SavedModel` from the above logs. We will be needing this for the subsequent steps. The warnings can be ignored. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "C0cRUvr6Yehj",
    "outputId": "76b23b46-63ca-4b56-cd97-32ccd1fef6d1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "836480\n"
     ]
    }
   ],
   "source": [
    "# Inspecting model size\n",
    "print(os.path.getsize(os.path.join(saved_model_dir, 'saved_model.pb')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_qjJqJvmZHX9"
   },
   "source": [
    "## TF Lite conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ec41CaavLTcq"
   },
   "outputs": [],
   "source": [
    "!pip install -q tensorflow==2.2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GGKCXOAbLYED"
   },
   "source": [
    "**Important**: Restart the runtime by selecting menu item, Runtime > Restart runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Ec-6Kg1GZDWF",
    "outputId": "f2d26a0f-8d4b-4b36-c0ea-3676dedac99d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4WeUyuemZTo_"
   },
   "outputs": [],
   "source": [
    "def convert_to_tflite(saved_model_path, tflite_model_path):\n",
    "    model = tf.saved_model.load(saved_model_path)\n",
    "    concrete_func = model.signatures[\n",
    "    tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]\n",
    "    concrete_func.inputs[0].set_shape([1, 256, 256, 3])\n",
    "    concrete_func.outputs[0].set_shape([1, 256, 256, 3])\n",
    "    converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])\n",
    "    converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\n",
    "\n",
    "    tflite_model = converter.convert()\n",
    "\n",
    "    with tf.io.gfile.GFile(tflite_model_path, 'wb') as f:\n",
    "        f.write(tflite_model)\n",
    "    print('Fixed-point Quantized model:', tflite_model_path, \n",
    "        'Size:', len(tflite_model) / 1024, \"kb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 479
    },
    "colab_type": "code",
    "id": "4doFf_-IZxQf",
    "outputId": "60d64e67-c68a-4178-e866-6c3028b76994"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv/conv2d/kernel:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv/conv2d/bias:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/ins_norm/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/ins_norm/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv_0/conv2d/kernel:0' shape=(3, 3, 64, 128) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv/conv2d/kernel:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv/conv2d/bias:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/ins_norm/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/ins_norm/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv_0/conv2d/kernel:0' shape=(3, 3, 64, 128) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv/conv2d/kernel:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv/conv2d/bias:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/ins_norm/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/ins_norm/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv_0/conv2d/kernel:0' shape=(3, 3, 64, 128) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv/conv2d/kernel:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv/conv2d/bias:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/ins_norm/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/ins_norm/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv_0/conv2d/kernel:0' shape=(3, 3, 64, 128) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv/conv2d/kernel:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv/conv2d/bias:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/ins_norm/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/ins_norm/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'generator_B/conv_0/conv2d/kernel:0' shape=(3, 3, 64, 128) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\n",
      "Fixed-point Quantized model: selfie2anime.tflite Size: 10488.6875 kb\n"
     ]
    }
   ],
   "source": [
    "convert_to_tflite('/tmp/tmp22_x9l4i/', 'selfie2anime.tflite') # Note that the path might change since we are using `tempfile`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KrUmi1NcaId1"
   },
   "source": [
    "Warnings can be ignored here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0XL4cchVaL_t"
   },
   "source": [
    "## Running inference with the TF Lite model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L3DXZ2epLqKH"
   },
   "source": [
    "### Gather an example image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "g1r-O71EaAJs",
    "outputId": "77a71c17-f38f-4c6d-bc04-1d3fc03f60d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-06-09 14:08:45--  https://pbs.twimg.com/profile_images/1235595938921459713/h26CpAPb_400x400.jpg\n",
      "Resolving pbs.twimg.com (pbs.twimg.com)... 192.229.173.16, 2606:2800:220:1410:489:141e:20bb:12f6\n",
      "Connecting to pbs.twimg.com (pbs.twimg.com)|192.229.173.16|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 22456 (22K) [image/jpeg]\n",
      "Saving to: ‘h26CpAPb_400x400.jpg’\n",
      "\n",
      "\r",
      "h26CpAPb_400x400.jp   0%[                    ]       0  --.-KB/s               \r",
      "h26CpAPb_400x400.jp 100%[===================>]  21.93K  --.-KB/s    in 0s      \n",
      "\n",
      "2020-06-09 14:08:45 (107 MB/s) - ‘h26CpAPb_400x400.jpg’ saved [22456/22456]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://pbs.twimg.com/profile_images/1235595938921459713/h26CpAPb_400x400.jpg\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z9QuJCyJLuWu"
   },
   "source": [
    "### View the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qqkxvRcNaXEC"
   },
   "outputs": [],
   "source": [
    "def load_image(path):\n",
    "  image_raw = tf.io.read_file(path)\n",
    "  image = tf.image.decode_image(image_raw, channels=3)\n",
    "  return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "fU1fD2gXabVf",
    "outputId": "39eab4aa-e8a9-4d7a-b989-2510b7c47172"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 400, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_image_original = load_image(\"h26CpAPb_400x400.jpg\")\n",
    "print(test_image_original.shape)\n",
    "plt.imshow(test_image_original)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CrQOCsrDLxkk"
   },
   "source": [
    "### Preprocess the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U6tU2T0uakkY"
   },
   "outputs": [],
   "source": [
    "def resize(image):\n",
    "    resized_image =  tf.image.resize(image, [256, 256], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n",
    "    resized_image = tf.cast(resized_image, tf.float32)\n",
    "    resized_image = tf.expand_dims(resized_image, 0)\n",
    "\n",
    "    return resized_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "7A8CIy48ao1R",
    "outputId": "b87711c5-09e1-488a-fd24-8a70d06363a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([1, 256, 256, 3])"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image_resized = resize(test_image_original)\n",
    "test_image_resized.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Sl7LmE6_L0tO"
   },
   "source": [
    "### Run inference on the preprocessed image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MGqjMGYkasoZ"
   },
   "outputs": [],
   "source": [
    "with tf.io.gfile.GFile('selfie2anime.tflite', 'rb') as f:\n",
    "    model_content = f.read()\n",
    "\n",
    "# Initialze TensorFlow Lite inpterpreter.\n",
    "interpreter = tf.lite.Interpreter(model_content=model_content)\n",
    "interpreter.allocate_tensors()\n",
    "input_index = interpreter.get_input_details()[0]['index']\n",
    "output = interpreter.tensor(interpreter.get_output_details()[0][\"index\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 250
    },
    "colab_type": "code",
    "id": "eljHWbPsbMWr",
    "outputId": "a3b08cec-1d29-441b-beb3-8dc63af4e55e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fee5040d240>"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set model input\n",
    "interpreter.set_tensor(input_index, test_image_resized)\n",
    "\n",
    "# Run inference\n",
    "interpreter.invoke()\n",
    "\n",
    "# Visualize results\n",
    "plt.subplot(121)\n",
    "plt.title('Selfie')\n",
    "plt.imshow(test_image_original)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title('Anime')\n",
    "plt.imshow(output()[0])"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Selfie2Anime_Model_Conversion.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
